BTSPAS: Bayesian Time-Stratified Population Analysis

Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.

Version: 2021.11.1
Imports: actuar, coda, data.table, ggplot2, ggforce, graphics, grDevices, gridExtra, plyr, reshape2, R2jags, scales, splines, stats, utils
Suggests: R.rsp
Published: 2021-10-11
Author: Carl J Schwarz and Simon J Bonner
Maintainer: Carl J Schwarz <cschwarz.stat.sfu.ca at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/cschwarz-stat-sfu-ca/BTSPAS
NeedsCompilation: no
SystemRequirements: JAGS
Citation: BTSPAS citation info
Materials: README NEWS
CRAN checks: BTSPAS results

Documentation:

Reference manual: BTSPAS.pdf
Vignettes: 01 Diagonal model
02 Diagonal model with multiple ages
03 Non-diagonal model
04 Non-diagonal with fall-back model
05 Bias from incomplete sampling
06 Interpolating run earlier and later

Downloads:

Package source: BTSPAS_2021.11.1.tar.gz
Windows binaries: r-devel: BTSPAS_2021.11.1.zip, r-release: BTSPAS_2021.11.1.zip, r-oldrel: BTSPAS_2021.11.1.zip
macOS binaries: r-release (arm64): BTSPAS_2021.1.1.tgz, r-release (x86_64): BTSPAS_2021.11.1.tgz, r-oldrel: BTSPAS_2021.11.1.tgz
Old sources: BTSPAS archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BTSPAS to link to this page.